Visualization of California oil spill incidents in 2008

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knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(tidyverse)
library(sf)
library(here)
library(tmap)
library(janitor)

Overview

The data used in this report includes locations of oil spills in California in 2008 as well as California county boundaries, which are merged together. Here I first look at where all of the 2008 oil spills were in California in an exploratory interactive visualization. Then, I construct a static choropleth map that shows which counties had the most oil spills in 2008.

Citations:

Oil spill data from CA DFW Oil Spill Incident Tracking, 2008. https://gis.data.ca.gov/datasets/7464e3d6f4924b50ad06e5a553d71086_0/data Shapefile data for the borders of California counties from the U.S. Census Bureau. https://data.ca.gov/dataset/ca-geographic-boundaries

Initial wrangling

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# Read in the data
# County shapes for CA
ca_counties_sf <- read_sf(here("data", "CA_Counties_TIGER2016.shp")) %>%
   clean_names()

# Oil spill events
oil_sf <- read_sf(here("data", "ds394.shp")) %>%
   clean_names()

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# Select only county names (for combining with oil spill dataset later)
ca_subset_sf <- ca_counties_sf %>% 
  select(county_name = name)

# Check counties CRS
#ca_subset_sf %>% st_crs()

# Change the oil spill CRS to match counties CRS
oil_3857_sf <- st_transform(oil_sf, st_crs(ca_counties_sf))

# Then check oil spill CRS to make sure it now matches
#oil_3857_sf %>% st_crs()

Exploratory interactive map: location of oil spill events

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# Set the viewing mode to "interactive"
tmap_mode(mode = "view")

# Make a map with county outlines, then add another shape layer for the oil spill records (added as dots)
tm_shape(ca_subset_sf) +
   tm_borders(lwd = 1, 
              col = "grey") +
   tm_shape(oil_3857_sf) +
   tm_dots(col="red2")

Figure 1. Exploratory map allowing users to explore the locations of individual oil spills in California. Outlines of different counties included to make locations of the spills more clear.

Choropleth map: inland oil spills by county

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# Join the two datasets
ca_oil_sf <- ca_subset_sf %>% 
   st_join(oil_3857_sf)

# Preparing oil spill counts by county
oil_counts_sf <- ca_oil_sf %>% 
   filter(inlandmari == "Inland") %>%
   group_by(county_name) %>%
   summarize(oil_records = sum(!is.na(oesnumber)))

# Adding a 0 count row for county that had none
modoc_geom <- ca_oil_sf %>%
   filter(county_name == "Modoc") %>%
   select(geometry) %>%
   distinct()

county <- "Modoc"
count <- 0
geom <- st_transform(modoc_geom, st_crs(oil_counts_sf))
new_row <- c(county, count, geom)

oil_counts_sf_fixed <- oil_counts_sf
oil_counts_sf_fixed[nrow(oil_counts_sf) + 1, ] <- new_row

# Make map of oil spill counts by county
ggplot(data = oil_counts_sf_fixed) +
   geom_sf(aes(fill = oil_records), 
           color = "white", 
           size = 0.1) +
   coord_sf(datum = NA) +
   scale_fill_gradientn(colors = c("lightgray","orange2","red2")) +
   theme_minimal() +
   labs(fill = "Number of oil spill records",
        title = "Inland oil spills by county in California, 2008") +
   theme(text = element_text(family = "Courier",
                             size = 12,
                             face = "bold"),
         axis.text = element_text(size = 10))

Figure 2. Choropleth map showing the number of inland oil spill events in each county. The more red a county is, the higher the number of oil spills. Los Angeles county contains the most inland spills.